With the continuous progress and development of the medical and health industry, drug procurement has gradually attracted more attention, and the drug bidding and procurement model in China is in a stage of gradual improvement. In this article, the historical evolution of drug centralized bidding and procurement policy in China is briefly introduced. By analyzing the current drug centralized bidding and procurement policy implemented in China, issues such as difficulty reasonably determining “quantity” in quantity procurement, lack of drug effective supervision of procurement policy, incomplete drug catalog procurement, and out-of-network procurement were found. Some possible feasible suggestions are also put forward, aiming at providing reference and ideas for further improvement of drug centralized bidding and procurement policy under the new situation, so as to ensure safety of drugs and benefit the people.
Objective To systematically review the effects of cost sharing in health insurance schemes, so as to provide evidence for better designing cost sharing ratio in health insurance scheme. Methods The search terms were discussed, tested and then chosen by subject matter experts and search coordinators. The total 20 databases including comprehensive scope, health, economics, sociology, and grey literatures were searched to retrieve all the description or evaluation studies on the effects of cost sharing, such as health services utilization, financial burden or moral hazard. The information from the included studies was extracted into a pre-designed data extraction form, and then it was analyzed and summarized. Results A total of 73 studies were included, covering 17 countries like Australia, Canada, and China, etc. The results of statistical analyses showed that, a) Cost sharing methods were applied to every kind of health insurance scheme. The target population included general population, the elder, the poor, those with chronic disease and children, etc. The services covered clinic, hospitalization, mental health, prevention and drug; and b) The effects brought from cost sharing included: From full fee to cost sharing scheme, the enrollee in developing countries increased their health care utilization, and decreased their financial burden. From full coverage to cost sharing, the utilization of health services decreased in developed countries, but the cost of health insurance could not be reduced, and some undesirable effects were brought due to the decrease of both essential health service utilization and essential drugs compliance.
Objective To explore the impact of Diagnosis-Intervention Packet (DIP) reform on the operation of pilot county-level hospital, analyze the challenges that hospitals may face in DIP reform, and propose strategies to adapt to the reform. Methods The settlement list data of inpatients insured by medical insurance for 2022 from a county-level tertiary public hospital in Jiuquan City, Gansu Province were collected, where DIP was planned to operate. The DIP payment was simulated, and the operational status of the hospital and departments after implementing DIP reform was analyzed based on enrollment status, cost deviation, length of stay, hospitalization expenses, and DIP payment as relevant indicators. Results Under the implementation of DIP payment, the overall enrollment rate of the hospital was 98.1%, including 85.4% in the core group, 7.0% in the comprehensive group, and 7.6% in the grassroots group. Normal costs accounted for 88.9%, deviation costs accounted for 11.1%, with high magnification cases accounting for 1.9% and low magnification cases accounting for 9.2%. The payment standard for all cases included in the hospital according to DIP was 15.464 million yuan, the total amount paid by the pooling fund was 19.986 million yuan, and the difference between DIP payment and payment by project was –4.522 million yuan. Conclusion There is a significant difference in the medical insurance payments received by county-level hospitals after implementing DIP payment, and there is an urgent need to adapt to the DIP payment reform as soon as possible.
Judging from the latest policies related to the medical insurance payment reform of the state and Sichuan province, the reform of medical insurance diagnosis-related group (DRG)/diagnosis-intervention packet (DIP) payment methods is imperative. The impact of DRG/DIP payment method reform on public hospitals is mainly analyzed from the aspects of hospital cost accounting and control, quality of filling in the first page of medical cases, coding accuracy, standard of medical practice, development of diagnosis and treatment technology innovation business, multi-departmental linkage mechanism, competition between hospitals, performance appraisal mechanism, and negotiation and communication mechanism. We should put forward hospital improvement strategies from the top-level design of the whole hospital and from the aspects of improving the quality of the first page of the cases and the quality of the coding, strengthening the cost accounting and control of the disease, carrying out in-hospital and out-of-hospital training, establishing a liaison model, finding gaps with benchmark hospitals, enhancing the core competitiveness of innovative technologies, and improving internal performance appraisal, etc., to promote the high-quality development of hospitals.
Objectives To evaluate the clinical outcomes and identify its associated factors in patients with acute coronary syndromes (ACS) in Tianjin city. Methods Data were obtained from Tianjin urban employee basic medical insurance database. Adult patients who were discharged alive after the first ACS-related hospitalization (the index hospitalization) during January, 2012 to December, 2014 and without malignant tumor were included. Clinical outcomes were measured by subsequent major adverse cardiovascular events (MACE) including hospitalization for myocardial infarction (MI) or stroke, all-cause death, or their composite endpoint. Cox model was used to explore the factors associated with MACE. Results 22 041 patients were identified, in which 9.5% experienced MACE during follow-up with a mean number of 1.3 MACEs. 3.1% of patients had MI, 5.7% had stroke and 1.4% had all-cause death. Among patients who experienced MACEs, the average time from index discharge to the 1st MACE was 143.2 days. Patients being older, male or had higher Charlson Comorbidity Index (CCI) were more likely to experience MACE. Patients who had prior stroke and prior all-cause hospitalization were also more likely to experience MACE, whereas patients who had prior angina, prior β-blockers utilization and received percutaneous coronary intervention (PCI) during index event were less likely to experience MACE. Conclusion Stroke is the most common type of MACE among ACS patients in Tianjin, China. Almost half of the 1st MACE occur within the 3 months after ACS. Patients who are older, male, have higher CCI or have prior stroke are at higher risk of MACE.
Objective To perform data-driven, assisted prediction of health insurance reimbursement ratios for the major thoracic surgery group in CHS-DRG, in addition to providing an optional solution for health insurance providers and medical institutions to accurately and effectively predict the references of health insurance payments for the patient group. Methods Using the information on major thoracic surgery cases from a large tertiary hospital in Sichuan province in 2020 as a sample, 70% of the total dataset was used as a training dataset and 30% as a test dataset. This data was used to predict health insurance spending through a multiple linear regression model and an improved machine learning method that is based on feature selection. Results When the number of filtered features was the same via three machine learning methods including random forest, logistic regression, and support vector machine, there was no significant difference in the prediction effectiveness. The model with the best prediction effect had an accuracy of 78.96%, sensitivity of 83.93%, specificity of 71.27%, precision of 0.818 8, AUC value of 0.841 4, and a Kappa value of 0.610 8. Conclusion The basic characteristics such as the number of disease diagnoses and surgical operations, as well as the age of patients affect the reimbursement ratio. The cost of materials, drugs, and treatments has a greater impact on the reimbursement ratio. The combined method of feature selection and machine learning outperforms traditional statistical linear models. When dealing with a larger dataset that has many features, selecting the right number can enhance the prediction ability and efficiency of the model.
Through reviewing the regulations on the right of emergency treatment of hospitals, we analyzed reasons of emergency treatment of hospitals, including uninformed patients and informed patients without consent in emergency situations, as well as the risk of emergency rescue of hospitals. We put forward how to consider the judgment of emergency situations, justification of emergency treatment of hospitals, and risk attribution. We suggested improving the related legislation and regulations, developing compulsory medical insurance and a medical rescue system on emergency treatment.
Health insurance system has been proved to be an effective way to promote the quality of health service in many countries. However, how to control health expenditure under health insurance system remains a problem to be resolved. Some developed countries like UK, Canada and Sweden linked their health technology assessment results with decision making and health insurance management, and made prominent achievements in both expenditure control and quality improvement. China is carrying out its health system reform and running a new health insurance project. Using the experiences of other countries is undoubtedly of great importance in developing and managing our health insurance system.
ObjectiveTo investigate the factors that influence Chinese residents, self-rated health and the effects of the multilevel health insurance system and neighborhood social capital on self-rated health. MethodsBased on the 2018 China labor-force dynamics survey data, and Stata 15.0 software was used to conduct χ2 test, univariate analysis and multiple logistic regression model were used to analyze the influencing factors of self-rated health of Chinese residents. An interaction model was used to analyze the interactive effects of the multilevel health insurance system and the social capital of the neighborhood on self-rated health. ResultsA total of 10 201 people were investigated in this study, and 39.20% of them were self-rated unhealthy. After adjusting for confounders, the results of the multivariate logistic regression model showed that having social health insurance (OR=0.8, 95%CI 0.7 to 1.0) and having neighborhood social capital (OR=0.7, 95%CI 0.6 to 0.8) were more inclined to self-rated health. In addition, the results showed that being male, having a college degree or higher, having a job, and drinking alcohol increased the risk of self-rated unhealthy (P<0.05); whereas being 45-59 years of age, 60 years of age or older, in the central and western regions, exercising regularly, and having a disease or injury within two weeks decreased the risk of self-rated unhealthy (P<0.05). There was a positive multiplicative interaction effect between health insurance and neighborhood social capital on residents’ self-rated health (univariate: OR=1.5, 95%CI 1.1 to 3.7, P<0.05; multivariate: OR=1.7, 95%CI 1.2 to 2.4, P<0.05), and negative additive interactions (RERI=−0.8, 95%CI −1.4 to −0.1; AP=−0.3, 95%CI −0.6 to −0.1; SI=0.6, 95%CI 0.5 to 0.8). ConclusionAttention should be paid to the self-rated health status of key populations through means such as health promotion and education, and healthy behavior lifestyles should be promoted. The health insurance system should be further improved, and attention should be paid to the role of social capital in the neighborhood, encouraging residents to actively build a good social neighborhood, and realizing the coordinated development of the multilevel health insurance system and the social capital in the neighborhood.